CN105631195B - A kind of gait analysis system and its method of wearable Multi-information acquisition - Google Patents
A kind of gait analysis system and its method of wearable Multi-information acquisition Download PDFInfo
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Abstract
The invention discloses a kind of gait analysis system and its method of wearable Multi-information acquisition, its feature includes：Flexible sensor module, 3-axis acceleration and gyro sensor, microprocessor, power supply module, transport module, locating module, client and Cloud Server etc..When measuring user's walking by flexible sensor, the gait data of foot bottom, after microprocessor analysis, sent by transport module to Cloud Server, and show gait situation in subscription client in real time.If user is in an emergency, user guardian can also receive warning message, and position customer location in real time, implement rescue.The present invention can allow guardian to understand the gait situation of custodial person at any time, and warning message and location information in real-time reception custodial person motion, so as to be foot patients ' recovery, the elderly walks the offers such as appearance monitoring, toddler and supports and help.
Description
Technical field
The present invention relates to intelligent wearable device technical field, more particularly to a kind of gait of wearable Multi-information acquisition
Analysis system and its method.
Background technology
With the improvement of living standards, people increasingly pay attention to the health condition of itself, particularly in terms of sport health,
People also increasingly wish that the moment understands oneself moving situation.With the rise of smart mobile phone, intelligent wearable technology and big number
According to the development of technology, the demand of people is also achieved.
Gait analysis can apply in sport health scientific domain.Such as：Pass through the normal walking posture to the elderly
The gait feature parameter of deterioration is extracted and proposes that what corresponding precautionary measures can efficiently reduce the elderly falls down number；
By being studied the gait feature of hemiplegic patient, so as to effectively analyze the current recovery of hemiplegic patient；
By the gait analysis to landform such as stair, level road, landslide, steps, and then effectively can be carried for the research of artificial intelligence leg
For more theoretical foundations；In addition, gait analysis can also be used in health science field to the patient with some diseases
It is identified, such as Parkinsonian, patients with cerebral palsy, diabetic.
At present, many researchs be there has also been for gait analysis both at home and abroad.Foots of the Tao Liu et al. in gauger, shank
Gyroscope and accelerometer are placed on bottom and knee, so as to measure the state of lower limb walking.Kevin Deschamps et al. exist
Foot bottom force-bearing situation when sole placement force sensor is to measure walking.The direction that these researchs provide for gait analysis,
Not iing is proposed can finer measuring method.
The content of the invention
In place of the present invention is overcomes the shortcomings of the prior art, it is proposed that it is a kind of can the wearable of man-machine interaction believe more
The gait analysis system and its method of breath fusion, the gait situation of the invention that guardian can be allowed to understand custodial person at any time, in real time
The warning message and location information in custodial person's motion are received, so as to be foot patients ' recovery, the elderly walks appearance monitoring, children
The offer such as learn to walk is supported and helped.
The present invention adopts the following technical scheme that to achieve the above object of the invention：
A kind of the characteristics of gait analysis system of wearable Multi-information acquisition of the present invention, includes：Flexible sensor module,
3-axis acceleration and gyro sensor, microprocessor, power supply module, transport module, locating module, client and cloud service
Device；Gait analysis device is formed by the 3-axis acceleration and gyro sensor, microprocessor, power supply module and transport module
And it is arranged at the arch of sole；The client includes guardian's client and subscription client；
The sensor assembly includes pliable pressure sensor and soft stretch sensor；
The pliable pressure sensor is arranged on the heel portion of shoe-pad, arch portion and front foot metacarpus, for gathering sole pressure
Force information simultaneously passes to the microprocessor by absorption type row's pin；
The soft stretch sensor is arranged between the arch portion of shoe-pad and front foot metacarpus, for gathering plantar flex degree
And pass to the microprocessor also by absorption type row's pin；
The 3-axis acceleration and gyro sensor are used for the acceleration gathered on three directions and angle of inclination and passed
Pass the microprocessor；
The locating module obtains the positional information of user and is sent to the microprocessor；
The microprocessor is calculated received foot force information, obtains Center of Pressure point information；And root
Analyzed according to Center of Pressure point information, plantar flex degree and the acceleration, obtain gait feature value；
The gait feature value is input to progress gait judgement, the gait shape of acquisition in neutral net by the microprocessor
State result is sent to the client by the transport module, or is sent to the Cloud Server by the transport module
Stored；The client is enabled to obtain gait state result from the Cloud Server；
The microprocessor by the transport module is sent to the cloud service after handling the positional information
Device is stored；
The microprocessor is analyzed the angle of inclination, judges whether to fall, if falling, passes through transport module
Warning message is sent to the Cloud Server；The Cloud Server gives the alarm information pushing to the guardian client again
End；
Guardian's client obtains customer position information by the Cloud Server.
The characteristics of gait analysis system of wearable Multi-information acquisition of the present invention, lies also in,
The composition of the pliable pressure sensor includes：Flexible PCB, copper electrode, micro-structural sensitive material and plastics
Film；
Flexible PCB is printed with the copper electrode on described, and covers the micro-structural on the surface of the copper electrode
Sensitive material；The plastic sheeting is covered on the micro-structural sensitive material；
The composition of the soft stretch sensor includes：Overlying plastic film, conductive silver glue, sensitive material and lower floor's modeling
Expect film；
Spaced two pieces of conductive silver glues are printed with lower floor's plastic sheeting as electrode；The sensitive material
Layer is pasted onto on lower floor's plastic sheeting by two pieces of conductive silver glues；Upper strata is covered on the surface of the sensitive material
Plastic sheeting.
The micro-structural sensitive material is arranged to pyramid array format, and is contacted with tower top with copper electrode.
The micro-structural sensitive material is that carbon black and graphite is dilute with 3：After 1 mass ratio mixing, then with 4% total matter
Amount fraction is filled in be molded in silicon rubber and obtained；
The sensitive material is that carbon black and graphite is dilute with 3：After 1 mass ratio mixing, then with 6% total mass fraction
It is filled in be molded in silicon rubber and obtains.
A kind of the characteristics of gait analysis method of wearable Multi-information acquisition of the present invention is to carry out as follows：
Step 1, origin O is set to a summit of the boundary rectangle of shoe-pad, two adjacent sides of the origin O are set respectively
It is set to X-axis and Y-axis, is used as Z axis, composition coordinate system O-XYZ with the direction of the boundary rectangle using vertical；
Step 2, in the coordinate system XOY, obtain n pliable pressure sensor position coordinates, be designated as { (x1,y1),
(x2,y2),…,(xi,yi),…,(xn,yn), (xi,yi) represent i-th of pliable pressure sensor position coordinates；1≤i≤n；
Step 3, using the n pliable pressure sensor obtain n foot force value, be designated as { P1,P2,…,Pi,…,
Pn}；PiRepresent the foot force value of i-th of pliable pressure sensor；
Step 4, utilize formula (1) and formula (2) acquisition Center of Pressure point information (xc,yc)：
Step 5, the plantar flex degree C by soft stretch sensor (2) acquisition tt；
Step 6, pass through the acceleration on three directions of 3-axis acceleration and gyro sensor acquisition tAnd the vector value S of t acceleration is obtained using formula (3)t：
When step 7, respectively collection normal gait and abnormal gait, Center of Pressure point information (xc,yc), plantar flex degree Ct、
Acceleration on three directionsAnd the vector value S of accelerationt, the respective seven kinds of characteristic values of this seven kinds of data,
For being trained to neutral net, gait analysis model is obtained；Seven kinds of characteristic values include maximum, minimum value, average, change
Scope, amplitude, variance and standard deviation；
Step 8, the setting Center of Pressure point information (xc,yc), plantar flex degree, the acceleration on three directions and
The respective seven kinds of characteristic values of vector value of acceleration are walked threshold value accordingly；
Step 9, using T as the sampling period, F is sample frequency, formed sampling time window；In sampling time window, gather by
Center of Pressure point information (xc,yc), plantar flex degree, the vector value respective seven of the acceleration on three directions and acceleration
The exercise data that kind characteristic value is formed；
Step 10, by the exercise data compared with set walking threshold value, judge in sampling time window, use
Whether family is in walking states；If in walking states, step 11 is performed；
Step 11, the exercise data inputted in the gait analysis model, so as to obtain in sampling time window
Gait state result.
Compared with the prior art, beneficial effects of the present invention are embodied in：
1st, the present invention is merged more heat transfer agents, and supports radio communication, and user's row is measured by flexible sensor
When walking, the gait data of foot bottom, after microprocessor analysis, sent by transport module to Cloud Server, and in real time with
Family client shows gait situation, if user is in an emergency, user guardian can also receive warning message, and position in real time
Customer location, implement rescue, so as to be foot patients ' recovery, the elderly's step appearance monitoring, toddler etc. provides a kind of long-term
Dress monitoring device.
2nd, pliable pressure sensor of the invention, is to make substrate with flexible PCB, using interdigitated electrode structure, with carbon black and
The dilute mixing filled silicon rubber of graphite uses small pyramid structure as sensing unit, and in cell surface.Compared to tradition
Pressure sensor, this pliable pressure sensing arrangement have the spies such as Grazing condition, thickness of thin, pressure-sensitive character are strong, range is big, stability is good
Point.It can be comfortably positioned on shoe-pad, not influence the wearing experience of wearer；And cost is relatively low, easily change.
3rd, soft stretch sensor of the invention, be using plastic sheeting as substrate, conductive silver glue is used as electrode, with carbon black with
The dilute mixing filled silicon rubber of graphite is as sensing unit.Compared to traditional stretch sensor.This soft stretch sensor has complete
The features such as flexibility, thickness of thin, stability are good.
4th, the gait analysis method of Multi-information acquisition of the invention, carries out segmentation to signal using time slip-window method and adopts
Sample, using Center of Pressure point method by the information fusion of multiple pressure sensors, in conjunction with stretch sensor and gyroscope, by three kinds
Seven kinds of feature fusions of sensor, realize the convergence analysis to sole gait data, reduce amount of calculation.
Brief description of the drawings
Fig. 1 is pliable pressure sensor plane structure of the present invention explanation figure；
Fig. 2 is pliable pressure sensor up-down structure of the present invention explanation figure；
Fig. 3 is soft stretch sensor plane structure of the present invention explanation figure；
Fig. 4 is soft stretch sensor up-down structure of the present invention explanation figure；
Fig. 5 is multi information measuring and analysis system of the present invention explanation figure.
Fig. 6 is present system block diagram；
Label in figure：1 pliable pressure sensor；1a flexible PCBs, 1b copper electrodes, 1c micro-structural sensitive materials；1d is moulded
Expect film；2 soft stretch sensors；2a overlying plastic films；2b conductive silver glues；2c sensitive materials；2d lower floors plastic sheeting；
3 absorption type contact pins；4 charging inlets.
Embodiment
In the present embodiment, as shown in fig. 6, a kind of gait analysis system of wearable Multi-information acquisition includes：Sensor
Module, 3-axis acceleration and gyro sensor, microprocessor, power supply module, transport module, locating module, client and cloud
Server；Gait analysis device is formed by 3-axis acceleration and gyro sensor, microprocessor, power supply module and transport module
And it is arranged at the arch of sole；Client includes guardian's client and subscription client.
Sensor assembly includes pliable pressure sensor 1 and soft stretch sensor 2；Pliable pressure sensor and flexible drawing
Stretch sensor to be distributed on shoe-pad, the distributing position of pliable pressure sensor and soft stretch sensor is as shown in Figure 5.
Pliable pressure sensor 1 is arranged on the heel portion of shoe-pad, arch portion and front foot metacarpus, for gathering foot force letter
Cease and pin 3 is arranged by absorption type and pass to microprocessor；Pliable pressure sensor structure figure is as shown in figure 1, with flexible PCB 1a
As substrate, copper electrode 1b uses interdigital structure；Sensitive material 1c is that carbon black and graphite is dilute with 3：1 mass ratio mixing
Afterwards, then with 4% total mass fraction it is filled in silicon rubber；Sensitive material after filling is poured into particular mold and shaped, is made
Sensitive material bottom surface has pyramid shape microwave structure, and is contacted with tower top with copper electrode 1b.Pliable pressure sensor construction is certainly
It is upper and lower as shown in Fig. 2 being followed successively by：Plastic sheeting 1d, sensitive material 1c, copper electrode 1b and flexible PCB 1a.
Soft stretch sensor 2 is arranged between the arch portion of shoe-pad and front foot metacarpus, for gathering plantar flex degree simultaneously
Microprocessor is passed to also by absorption type row's pin 3；Soft stretch sensor construction such as Fig. 3, electrode is made using conductive silver glue, it is quick
Sense material layer 2c is that carbon black and graphite is dilute with 3：After 1 mass ratio mixing, then silicon rubber is filled in 6% total mass fraction
Middle shaping obtains.Soft stretch sensor construction is from top to bottom as shown in figure 4, be followed successively by：Overlying plastic film 2a, sensitive material
Layer 2c, conductive silver glue 2b and lower floor plastic sheeting 2d.
Lithium battery and charging module, 3-axis acceleration sensor and gyroscope, locating module, bluetooth module, GPRS module
As primary module, arch bottom is positioned over.Sensor assembly and primary module are arranged the interface of pin 3 by absorption type and connected.
Acceleration that 3-axis acceleration and gyro sensor are used to gather on three directions and angle of inclination simultaneously pass to
Microprocessor；
Locating module obtains the positional information of user and is sent to microprocessor；
Microprocessor is calculated received foot force information, obtains Center of Pressure point information；And according to pressure
Power central point information, plantar flex degree and acceleration are analyzed, and are obtained gait feature value, are as follows specifically
Carry out：
Step 1, origin O is set to a summit of the boundary rectangle of shoe-pad, origin O two adjacent sides are respectively set to
X-axis and Y-axis, Z axis is used as using the direction vertically with boundary rectangle, forms coordinate system O-XYZ；
Step 2, in coordinate system XOY, obtain n pliable pressure sensor position coordinates, be designated as { (x1,y1),(x2,
y2),…,(xi,yi),…,(xn,yn), (xi,yi) represent i-th of pliable pressure sensor position coordinates；1≤i≤n；
Step 3, using n pliable pressure sensor n foot force value is obtained, be designated as { P1,P2,…,Pi,…,Pn}；Pi
Represent the foot force value of i-th of pliable pressure sensor；
Step 4, utilize formula (1) and formula (2) acquisition Center of Pressure point information (xc,yc)：
Step 5, the plantar flex degree C by the acquisition t of soft stretch sensor 2t；
Step 6, pass through the acceleration on three directions of 3-axis acceleration and gyro sensor acquisition tAnd the vector value S of t acceleration is obtained using formula (3)t：
When step 7, respectively collection normal gait and abnormal gait, Center of Pressure point information (xc,yc), plantar flex degree Ct、
Acceleration on three directionsAnd the vector value S of accelerationt, the respective seven kinds of characteristic values of this seven kinds of data,
For being trained to neutral net, gait analysis model is obtained；Seven kinds of characteristic values include maximum, minimum value, average, change
Scope, amplitude, variance and standard deviation；
This seven kinds of characteristic values represent respectively：
Maximum：Maximum in all sampled points；
Minimum value：Maximum in all sampled points；
Average：The average value of all sampled points；
Excursion：The difference of maxima and minima in all sampled points；
Amplitude：The difference of maximum and average in all sampled points；
Variance：The variance of all sampled points；
Standard deviation：The standard deviation of all sampled points.
Step 8, setting Center of Pressure point information (xc,yc), plantar flex degree Ct, acceleration on three directionsAnd the vector value S of accelerationtRespective seven kinds of characteristic values are walked threshold value accordingly；
Step 9, using T as the sampling period, F is sample frequency, formed sampling time window；In sampling time window, gather by
Center of Pressure point information (xc,yc), plantar flex degree Ct, acceleration on three directionsAnd the arrow of acceleration
Value StThe exercise data that respective seven kinds of characteristic values are formed；
Step 10, by exercise data compared with set walking threshold value, judge in sampling time window, Yong Hushi
It is no in walking states；If in walking states, step 11 is performed；
Step 11, by exercise data input gait analysis model in, so as to obtain the gait state in sampling time window
As a result.
Gait feature value is input to progress gait judgement in neutral net by microprocessor.Structure three layers of BP networks be：
First layer is input layer, is made up of 49 input nodes, respectively it is corresponding 49 input component be corresponding in turn to for seven kinds of data it is each
From seven kinds of characteristic values maximum, minimum value, average, excursion, amplitude, variance, standard deviation.49 input components are formed
1 input vector X is：；The second layer is hidden layer；Third layer is output layer, is made up of 2 output nodes, two output nodes
Institute respectively corresponding to 2 output components represent that gait is correct and abnormal gait, 2 output components form 1 output vector Y successively
For：, to be characterized as gait correct status, to be characterized as abnormal gait state.
System block diagram is as shown in fig. 6, the gait state result obtained is sent to client by transport module, or passes through
Transport module is sent to Cloud Server and stored；Client is enabled to obtain gait state result from Cloud Server；
Microprocessor is sent to Cloud Server by transport module after handling positional information and stored；
Microprocessor is analyzed angle of inclination, judges whether to fall, if falling, is sent and alarmed by transport module
Information is to Cloud Server；Cloud Server gives alarm information pushing to guardian's client again；
Guardian's client obtains customer position information by Cloud Server.
In specific implementation, according to foot's acceleration and the mechanical periodicity of foot force, it is possible to achieve step function.
Cloud server preserves the initial data of user, and generates files on each of customers.To the aggregation of data in a period of time point
Analysis, analyze the change of user movement situation.
Cell-phone customer terminal is different according to user and guardian role, has different authorities, user can be real-time in mobile phone terminal
The gait situation of oneself is checked, reminds the incorrect posture in user movement.Guardian can check location data, can also connect
Receive the hazard condition warning applications of user.
Claims (5)
1. a kind of gait analysis system of wearable Multi-information acquisition, its feature include：Flexible sensor module, three axles accelerate
Degree and gyro sensor, microprocessor, power supply module, transport module, locating module, client and Cloud Server；By described
3-axis acceleration and gyro sensor, microprocessor, power supply module and transport module form gait analysis device and are arranged on
At the arch of sole；The client includes guardian's client and subscription client；
The sensor assembly includes pliable pressure sensor (1) and soft stretch sensor (2)；
The pliable pressure sensor (1) is arranged on the heel portion of shoe-pad, arch portion and front foot metacarpus, for gathering foot force
Information simultaneously passes to the microprocessor by absorption type row's pin (3)；
The soft stretch sensor (2) is arranged between the arch portion of shoe-pad and front foot metacarpus, for gathering plantar flex degree
And arrange pin (3) also by absorption type and pass to the microprocessor；
The 3-axis acceleration and gyro sensor are used for the acceleration gathered on three directions and angle of inclination and passed to
The microprocessor；
The locating module obtains the positional information of user and is sent to the microprocessor；
The microprocessor is calculated received foot force information, obtains Center of Pressure point information；And according to institute
State Center of Pressure point information, plantar flex degree and acceleration to be analyzed, obtain gait feature value；
The gait feature value is input to progress gait judgement, the gait state knot of acquisition in neutral net by the microprocessor
Fruit is sent to the client by the transport module, or is sent to the Cloud Server by the transport module and carries out
Storage；The client is enabled to obtain gait state result from the Cloud Server；
The microprocessor is sent to the Cloud Server by the transport module after handling the positional information and entered
Row storage；
The microprocessor is analyzed the angle of inclination, judges whether to fall, if falling, is sent by transport module
Warning message gives the Cloud Server；The Cloud Server gives the alarm information pushing to guardian's client again；
Guardian's client obtains customer position information by the Cloud Server.
2. the gait analysis system of wearable Multi-information acquisition according to claim 1, it is characterized in that：The flexible pressure
The composition of force snesor (1) includes：Flexible PCB (1a), copper electrode (1b), micro-structural sensitive material (1c) and plastic sheeting
(1d)；
The copper electrode (1b) is printed with the flexible PCB (1a), and described in the covering of the surface of the copper electrode (1b)
Micro-structural sensitive material (1c)；The plastic sheeting (1d) is covered on the micro-structural sensitive material (1c)；
The composition of the soft stretch sensor (2) includes：Overlying plastic film (2a), conductive silver glue (2b), sensitive material
(2c) and lower floor's plastic sheeting (2d)；
Spaced two pieces of conductive silver glues (2b) are printed with lower floor's plastic sheeting (2d) and are used as electrode；The sensitivity
Material layer (2c) is pasted onto on lower floor's plastic sheeting (2d) by two pieces of conductive silver glues (2b)；In the sensitive material
The surface covering overlying plastic film (2a) of layer (2c).
3. the gait analysis system of wearable Multi-information acquisition according to claim 2, it is characterized in that：The micro-structural
Sensitive material (1c) is arranged to pyramid array format, and is contacted with tower top with copper electrode (1b).
4. the gait analysis system of wearable Multi-information acquisition according to claim 2, it is characterized in that：The micro-structural
Sensitive material (1c) is that carbon black and graphite is dilute with 3：After 1 mass ratio mixing, then silicon is filled in 4% total mass fraction
It is molded and obtains in rubber；
The sensitive material (2c) is that carbon black and graphite is dilute with 3：After 1 mass ratio mixing, then with 6% total mass fraction
It is filled in be molded in silicon rubber and obtains.
A kind of 5. gait analysis method of wearable Multi-information acquisition, it is characterized in that carrying out as follows：
Step 1, origin O is set to a summit of the boundary rectangle of shoe-pad, two adjacent sides of the origin O are respectively set to
X-axis and Y-axis, Z axis, composition coordinate system O-XYZ are used as using the vertical and direction of the boundary rectangle；
Step 2, in coordinate system XOY, obtain n pliable pressure sensor position coordinates, be designated as { (x1,y1),(x2,
y2),…,(xi,yi),…,(xn,yn), (xi,yi) represent i-th of pliable pressure sensor position coordinates；1≤i≤n；
Step 3, using the n pliable pressure sensor obtain n foot force value, be designated as { P1,P2,…,Pi,…,Pn}；Pi
Represent the foot force value of i-th of pliable pressure sensor；
Step 4, utilize formula (1) and formula (2) acquisition Center of Pressure point information (xc,yc)：
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<mo>/</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>n</mi>
</munderover>
<msub>
<mi>P</mi>
<mi>i</mi>
</msub>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>2</mn>
<mo>)</mo>
</mrow>
</mrow>
Step 5, the plantar flex degree C by soft stretch sensor (2) acquisition tt；
Step 6, pass through the acceleration on three directions of 3-axis acceleration and gyro sensor acquisition t
And the vector value S of t acceleration is obtained using formula (3)t：
<mrow>
<msub>
<mi>S</mi>
<mi>t</mi>
</msub>
<mo>=</mo>
<msqrt>
<mrow>
<msup>
<mrow>
<mo>(</mo>
<msubsup>
<mi>a</mi>
<mi>x</mi>
<mi>t</mi>
</msubsup>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
<mo>+</mo>
<msup>
<mrow>
<mo>(</mo>
<msubsup>
<mi>a</mi>
<mi>y</mi>
<mi>t</mi>
</msubsup>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
<mo>+</mo>
<msup>
<mrow>
<mo>(</mo>
<msubsup>
<mi>a</mi>
<mi>z</mi>
<mi>t</mi>
</msubsup>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
</mrow>
</msqrt>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>3</mn>
<mo>)</mo>
</mrow>
</mrow>
When step 7, respectively collection normal gait and abnormal gait, Center of Pressure point information (xc,yc), plantar flex degree Ct, three
Acceleration on directionAnd the vector value S of accelerationt, the respective seven kinds of characteristic values of this seven kinds of data, it is used for
Neutral net is trained, obtains gait analysis model；Seven kinds of characteristic values include maximum, minimum value, average, change model
Enclose, amplitude, variance and standard deviation；
Step 8, the setting Center of Pressure point information (xc,yc), plantar flex degree, the acceleration on three directions and acceleration
The respective seven kinds of characteristic values of vector value of degree are walked threshold value accordingly；
Step 9, using T as the sampling period, F is sample frequency, formed sampling time window；In sampling time window, gather by pressure
Central point information (xc,yc), plantar flex degree, the respective seven kinds of spies of the vector value of the acceleration on three directions and acceleration
The exercise data that value indicative is formed；
Step 10, by the exercise data compared with set walking threshold value, judge in sampling time window, Yong Hushi
It is no in walking states；If in walking states, step 11 is performed；
Step 11, the exercise data inputted in the gait analysis model, so as to obtain the gait in sampling time window
State outcome.
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